Classification of sleep apnea types using wavelet packet analysis of short-term ECG signals.
Objective: Obstructive sleep apnea (OSA) causes a pause in airflow with reduced breathing effort. In contrast, central sleep apnea (CSA) event is not accompanied with breathing effort. The aim of this study is to differentiate CSA and OSA events using wavelet packet analysis and support vector machi...
| Published in: | Journal of Clinical Monitoring & Computing Vol. 26; no. 1; pp. 1 - 12 |
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| Main Authors: | , , , , , |
| Format: | research Journal Article |
| Published: |
Springer Nature
Feb2012
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104508386&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104508386 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13871307 OHC jtl: Journal of Clinical Monitoring & Computing issn: 13871307 maglogo: N pubinfo: dt: Feb2012 vid: 26 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104508386 NLM22190269 2011443380 10.1007/s10877-011-9323-z NLM22190269 104508386 ppf: 1 ppct: 11 formats: fmt: @attributes: type: P tig: atl: Classification of sleep apnea types using wavelet packet analysis of short-term ECG signals. aug: au: Gubbi J Khandoker A Palaniswami M Gubbi, Jayavardhana Khandoker, Ahsan Palaniswami, Marimuthu affil: Department of Electrical and Electronic Engineering, University of Melbourne, Melbourne, VIC, 3010, Australia sug: subj: Electrocardiography Sleep Apnea, Central Diagnosis Sleep Apnea, Obstructive Diagnosis Human Middle Age Polysomnography Sensitivity and Specificity Signal Processing, Computer Assisted Sleep Apnea, Central Classification Sleep Apnea, Obstructive Classification Middle Aged: 45-64 years ab: Objective: Obstructive sleep apnea (OSA) causes a pause in airflow with reduced breathing effort. In contrast, central sleep apnea (CSA) event is not accompanied with breathing effort. The aim of this study is to differentiate CSA and OSA events using wavelet packet analysis and support vector machines of ECG signals over 5 s period.Methods: Eight level wavelet packet analysis was performed on each 5 s clip using Daubechies (DB3) mother wavelet and for comparison discrete wavelet analysis was performed using Symlet (SYM3) wavelets. The choice of wavelet basis function was based on a grid search using Daubechies, Symlet and biorthogonal wavelets with decomposition levels varying between 2 and 5. Support vector machine is used for two-class classification. Out of 29 overnight polysomnographic studies, 23 of them were used in the training phase and 6 patients were used for independent testing.Results: The proposed algorithm is shown to perform better in classifying CSA and OSA with wavelet packet features (accuracy-91%, sensitivity-88.14% and specificity-91.11%) than with the traditional wavelet decomposition based features (accuracy-83.79%, sensitivity-89.18% and specificity-83.59%). The independent test resulted in overall classification accuracy, sensitivity and specificity of 91.08, 91.02 and 91.09% respectively using wavelet packet analysis.Conclusions: The classification result indicates the possibility of non-invasively classifying CSA and OSA events based on shorter segments of ECG signals. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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